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Clinical Chemistry, Vol 19, 49-57, Copyright © 1973 by the American Association for Clinical Chemistry
1 From the Department of Medicine and the Clinical Laboratories, University of Wisconsin Center for Health Sciences, Madison, Wis. 53706
We have studied the usefulness of common statistical tests as applied to method comparison studies. We simulated different types of errors in test sets of data to determine the sensitivity of different statistical parameters. Least-squares parameters (slope of least-squares line, its y intercept, and the standard error of estimate in the y direction) provide specific estimates of proportional, constant, and random errors, but comparison data must be presented graphically to detect limitations caused by nonlinearity and errant points. t-test parameters ( bias, standard deviation of difference) provide estimates of constant and random errors, but only when proportional error is absent. Least-squares analysis can estimate proportional error and should be considered a prerequisite to t-test analysis. The correlation coefficient (r) is sensitive only to random error, but is not easily interpreted. Values for r, t, and F are not useful in making decisions on the acceptability of performance. These decisions should be judgments on the errors that are tolerable. Statistical tests can be applied in a manner that provides specific estimates of these errors
Accepted on October 7, 1972
The following articles in journals at HighWire Press have cited this article:
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D. G. Altman and J. M. Bland Commentary on Quantifying Agreement between Two Methods of Measurement Clin. Chem., May 1, 2002; 48(5): 801 - 802. [Full Text] [PDF]
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K. Linnet Necessary Sample Size for Method Comparison Studies Based on Regression Analysis Clin. Chem., June 1, 1999; 45(6): 882 - 894. [Abstract] [Full Text] [PDF]
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K. Linnet Limitations of the Paired t-Test for Evaluation of Method Comparison Data Clin. Chem., February 1, 1999; 45(2): 314 - 315. [Full Text] [PDF]
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D. Stockl, K. Dewitte, and L. M. Thienpont Validity of linear regression in method comparison studies: is it limited by the statistical model or the quality of the analytical input data? Clin. Chem., November 1, 1998; 44(11): 2340 - 2346. [Abstract] [Full Text] [PDF]
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